A hybrid recommender system for health supplement e-commerce based on customer data implicit ratings
Keikhosrokiani, Pantea; Fye, Goh Man (2023-10-21)
Avaa tiedosto
Sisältö avataan julkiseksi: 21.10.2024
Springer
21.10.2023
Keikhosrokiani, P., Fye, G.M. A hybrid recommender system for health supplement e-commerce based on customer data implicit ratings. Multimed Tools Appl 83, 45315–45344 (2024). https://doi.org/10.1007/s11042-023-17321-6
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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
https://rightsstatements.org/vocab/InC/1.0/
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe20231026141511
https://urn.fi/URN:NBN:fi-fe20231026141511
Tiivistelmä
Abstract
The personalized product preference and decision-making recommendation systems are highly demanded to handle big data and to increase service quality of the e-commerce platforms in the competitive industries. Previous recommender systems were hard coded and only extracted items from the same category. With this tactic, customers are limited to viewing only one category of products; items with several categories cannot be viewed. A concern for the e-commerce sector, particularly in the healthcare and pharmaceutical industries, is the growth of consumer preferences, the issue of cold starts, and the huge number of stocks holding units for new items. Therefore, this study aims to develop a product recommendation system for an e-commerce platform which deals with health supplements. For this reason, collaborative and content-based filtering are combined to propose a hybrid recommender system. In the proposed hybrid model, user’s actions are converted into implicit rating weightage first. Then, to tackle the problem of increasing customer preferences, collaborative filtering is used to generate user’s rating for warm-start items. Moreover, content-based filtering is used to solve cold start problem by recommending products to the users based on the similarity of the products regardless of user profile. Term frequency- inverse document frequency (TF-IDF) algorithm is adopted to weight the feature from the dataset first, then it creates step-by-step cosine similarity table. Finally, the proposed hybrid model is evaluated based on error metrices, ranking metrices, and business metrics and then compared based on the standard benchmarking algorithms. The best algorithm is selected to be used for the system development. Finally, the proposed hybrid model is developed and integrated into the real online e-commerce platform for healthcare company to handle the large number of stocks keeping units, cold start issues, and increasing customer preferences. This study can assist the healthcare companies to recommend relevant products to their customers and to help them stay competitive in healthcare e-commerce industry.
The personalized product preference and decision-making recommendation systems are highly demanded to handle big data and to increase service quality of the e-commerce platforms in the competitive industries. Previous recommender systems were hard coded and only extracted items from the same category. With this tactic, customers are limited to viewing only one category of products; items with several categories cannot be viewed. A concern for the e-commerce sector, particularly in the healthcare and pharmaceutical industries, is the growth of consumer preferences, the issue of cold starts, and the huge number of stocks holding units for new items. Therefore, this study aims to develop a product recommendation system for an e-commerce platform which deals with health supplements. For this reason, collaborative and content-based filtering are combined to propose a hybrid recommender system. In the proposed hybrid model, user’s actions are converted into implicit rating weightage first. Then, to tackle the problem of increasing customer preferences, collaborative filtering is used to generate user’s rating for warm-start items. Moreover, content-based filtering is used to solve cold start problem by recommending products to the users based on the similarity of the products regardless of user profile. Term frequency- inverse document frequency (TF-IDF) algorithm is adopted to weight the feature from the dataset first, then it creates step-by-step cosine similarity table. Finally, the proposed hybrid model is evaluated based on error metrices, ranking metrices, and business metrics and then compared based on the standard benchmarking algorithms. The best algorithm is selected to be used for the system development. Finally, the proposed hybrid model is developed and integrated into the real online e-commerce platform for healthcare company to handle the large number of stocks keeping units, cold start issues, and increasing customer preferences. This study can assist the healthcare companies to recommend relevant products to their customers and to help them stay competitive in healthcare e-commerce industry.
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